Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

Abstract

Abstract. In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As target for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period . These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071-2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the largeCorrespondence to: R. Rojas

HESS Opinions "Should we apply bias correction to global and regional climate model data?"

“…comparability of their land-atmosphere feedback functioning. This is usually not fulfilled (Rojas et al, 2011; see also Sect. 5.1).…”

“…This is well known and has been recognized by many authors, e.g. Wilby et al (2000), Wood et al (2004), Randall et al (2007), Piani et al (2010), Hagemann et al (2011, Rojas et al (2011), Haddeland et al (2012), Johnson and Sharma (2012). To overcome this problem, post-processing of either GCM or RCM output by correcting with and towards observations has become a standard procedure in climate change impact studies (CCIS).…”

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

“…(4) to Eq. (7) were all used by Piani et al (2010b) and some of them have been further explored in follow up studies (Dosio and Paruolo, 2011;Rojas et al, 2011). Following Piani et al (2010b), all parametric transformations were fitted to the fraction of the CDF corresponding to observed wet days (P o > 0) by minimising the residual sum of squares.…”

“…Among the most popular approaches are statistical transformations that aim to adjust (selected aspects of) the distribution of RCM (e.g. Ashfaq et al, 2010;Dosio and Paruolo, 2011;Rojas et al, 2011;Themeßl et al, 2011;Sunyer et al, 2012) and global circulation model (GCM) (e.g. Wood et al, 2004;Ines and Hansen, 2006;Boé et al, 2007;Li et al, 2010;Piani et al, 2010a,b;Johnson and Sharma, 2011) precipitation such that its new distribution resembles observations.…”

Bias Correction of MRI-WRF Dynamic Downscaling Datasets

“…Promising data exists to assess long-term, (longer than several decades, changes of extreme weather events); however, these data are not free of problems. Various sources of uncertainties such as the model's compactness, parameters disturbances and initial/boundary condition bias in the high-resolution model output are unavoidable (Christensen et al 2008;Rojas et al 2011). …”